Malware Detection System Based on Static and Dynamic Analysis Using Machine Learning
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Дата
2023
Автори
Науковий керівник
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Видавець
Igor Sikorsky Kyiv Polytechnic Institute
Анотація
Cyber wars and cyber attacks are an urgent problem in the global digital environment. Based on existing popular detection methods, malware authors are creating ever more advanced and sophisticated malware. Therefore, this study aims to create a malware analysis system that uses both dynamic and static analysis. Our system is based on a machine learning method - support vector machine. The set of data used was collected from various Internet sources. It consists of 257 executable files in .exe format, 178 of which are malicious and 79 are benign. We use 5 different types of data representation: binary information, trace instructions, control flow graph, information obtained from the dynamic operation of the file, and file metadata. Then, using multiple kernel learning, we combine all data views and create one summative machine learning model.
Опис
Ключові слова
malware detection, malware dynamic analysis, feature selection, multiple kernel learning
Бібліографічний опис
Nafiiev, A. Malware Detection System Based on Static and Dynamic Analysis Using Machine Learning / Alan Nafiiev, Andrii Rodionov // Theoretical and Applied Cybersecurity : scientific journal. – 2023. – Vol. 5, Iss. 2. – Pp. 97–104. – Bibliogr. 11 ref.